Security and Privacy for Big Data: A Systematic Literature Review

Big data is currently a hot research topic, with four million hits on Google scholar in October 2016. One reason for the popularity of big data research is the knowledge that can be extracted from analyzing these large data sets. However, data can contain sensitive information, and data must therefore be sufficiently protected as it is stored and processed. Furthermore, it might also be required to provide meaningful, proven, privacy guarantees if the data can be linked to individuals. To the best of our knowledge, there exists no systematic overview of the overlap between big data and the area of security and privacy. Consequently, this review aims to explore security and privacy research within big data, by outlining and providing structure to what research currently exists. Moreover, we investigate which papers connect security and privacy with big data, and which categories these papers cover. Ultimately, is security and privacy research for big data different from the rest of the research within the security and privacy domain? To answer these questions, we perform a systematic literature review (SLR), where we collect recent papers from top conferences, and categorize them in order to provide an overview of the security and privacy topics present within the context of big data. Within each category we also present a qualitative analysis of papers representative for that specific area. Furthermore, we explore and visualize the relationship between the categories. Thus, the objective of this review is to provide a snapshot of the current state of security and privacy research for big data, and to discover where further research is required

Optimizing Batch Linear Queries under Exact and Approximate Differential Privacy

Differential privacy is a promising privacy-preserving paradigm for statistical query processing over sensitive data. It works by injecting random noise into each query result, such that it is provably hard for the adversary to infer the presence or absence of any individual record from the published noisy results. The main objective in differentially private query processing is to maximize the accuracy of the query results, while satisfying the privacy guarantees. Previous work, notably \cite{LHR+10}, has suggested that with an appropriate strategy, processing a batch of correlated queries as a whole achieves considerably higher accuracy than answering them individually. However, to our knowledge there is currently no practical solution to find such a strategy for an arbitrary query batch; existing methods either return strategies of poor quality (often worse than naive methods) or require prohibitively expensive computations for even moderately large domains. Motivated by this, we propose low-rank mechanism (LRM), the first practical differentially private technique for answering batch linear queries with high accuracy. LRM works for both exact (i.e., $\epsilon$-) and approximate (i.e., ($\epsilon$, $\delta$)-) differential privacy definitions. We derive the utility guarantees of LRM, and provide guidance on how to set the privacy parameters given the user's utility expectation. Extensive experiments using real data demonstrate that our proposed method consistently outperforms state-of-the-art query processing solutions under differential privacy, by large margins.Comment: ACM Transactions on Database Systems (ACM TODS). arXiv admin note: text overlap with arXiv:1212.230

Security and Privacy for Big Data: A Systematic Literature Review

Abstract-Big data is currently a hot research topic, with four million hits on Google scholar in October 2016. One reason for the popularity of big data research is the knowledge that can be extracted from analyzing these large data sets. However, data can contain sensitive information, and data must therefore be sufficiently protected as it is stored and processed. Furthermore, it might also be required to provide meaningful, proven, privacy guarantees if the data can be linked to individuals. To the best of our knowledge, there exists no systematic overview of the overlap between big data and the area of security and privacy. Consequently, this review aims to explore security and privacy research within big data, by outlining and providing structure to what research currently exists. Moreover, we investigate which papers connect security and privacy with big data, and which categories these papers cover. Ultimately, is security and privacy research for big data different from the rest of the research within the security and privacy domain? To answer these questions, we perform a systematic literature review (SLR), where we collect recent papers from top conferences, and categorize them in order to provide an overview of the security and privacy topics present within the context of big data. Within each category we also present a qualitative analysis of papers representative for that specific area. Furthermore, we explore and visualize the relationship between the categories. Thus, the objective of this review is to provide a snapshot of the current state of security and privacy research for big data, and to discover where further research is required